This package provides a data frame to xlsx exporter based on libxlsxwriter.

This package provides an interface to Amazon Web Services end user computing services, including collaborative document editing, mobile intranet, and more.

This package provides implementations of apply(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

This package is a port of the new matplotlib color maps (viridis, magma, plasma and inferno) to R. matplotlib is a popular plotting library for Python. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness. This is the lite version of the more complete viridis package.

This package provides cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key.

This package provides functions for Bayesian A/B testing including prior elicitation options based on Kass and Vaidyanathan (1992) doi:10.1111/j.2517-6161.1992.tb01868.x.

This package lets you import Excel files into R. It supports .xls via the embedded libxls C library and .xlsx via the embedded RapidXML C++ library.

This package provides methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields.

This package provides an extensible framework for automatically placing direct labels onto multicolor plots. Label positions are described using positioning methods that can be re-used across several different plots. There are heuristics for examining trellis and ggplot objects and inferring an appropriate positioning method.

This package provides visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).

This package provides basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.

This package provides functions that read and solve linear inverse problems (food web problems, linear programming problems).

This package provides a variety of descriptive multivariate analyses with the singular value decomposition, such as principal components analysis, correspondence analysis, and multidimensional scaling. See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.

This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done.

This package provides methods for calculating accurate numerical first and second order derivatives.

This package provides tools used by organizational researchers for the analysis of multilevel data. It includes four broad sets of tools.

1. functions for estimating within-group agreement and reliability indices.

2. functions for manipulating multilevel and longitudinal (panel) data.

3. simulations for estimating power and generating multilevel data.

4. miscellaneous functions for estimating reliability and performing simple calculations and data transformations.

This package provides a set of tools to perform Quantitative Trait Locus (QTL) analysis in experimental crosses. It is a reimplementation of the R/qtl package to better handle high-dimensional data and complex cross designs. Broman et al. (2018) <doi:10.1534/genetics.118.301595>.

The CommonMark specification defines a rationalized version of markdown syntax. This package uses the cmark reference implementation for converting markdown text into various formats including HTML, LaTeX and groff man. In addition, it exposes the markdown parse tree in XML format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text.

Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.

This package analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

This package provides an R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition. It runs the equivalent of the leidenalg find_partition() function. This package includes the required source code files from the official leidenalg distribution and functions from the R igraph package.

This package provides an implementation of the ACME estimator, described in Wolpert (2015), ACME: A Partially Periodic Estimator of Avian & Chiropteran Mortality at Wind Turbines. Unlike most other models, this estimator supports decreasing-hazard Weibull model for persistence; decreasing search proficiency as carcasses age; variable bleed-through at successive searches; and interval mortality estimates. The package provides, based on search data, functions for estimating the mortality inflation factor in Frequentist and Bayesian settings.

This package provides bindings to libsodium: a library for encryption, decryption, signatures, password hashing and more. Sodium uses curve25519, a Diffie-Hellman function by Daniel Bernstein, which has become very popular after it was discovered that the NSA had backdoored Dual EC DRBG.

The package implements basic and high-level functions for reading, writing, manipulating, analyzing and modeling of gridded spatial data. Processing of very large files is supported.